Moore Capital - Consumer Credit Portfolio Analysis

Quant Modeler Case Study – Consumer Credit & Cashflow Modeling

Report Date: November 09, 2025

Executive Summary

Key Findings:

1. Data Overview

1.1 Data Quality Assessment

During the data exploration phase, several data quality issues were identified and addressed:

Loan Tape Dataset Issues:

Loan Performance Dataset Issues:

Data Cleaning Actions Taken:

1.2 Portfolio Composition by Program

Loan Count Avg Loan Term Avg MDR (%) Avg Int Rate (%) Avg FICO Avg Approved Amount ($)
P1 31,957 21.49 4.67 8.05 771 5,437
P2 32,785 21.71 5.37 18.20 696 4,278
P3 18,493 16.12 7.62 25.32 606 3,200
Overall 83,235 20.39 5.60 15.88 705 4,483

1.3 Portfolio Trends & Credit Quality Evolution

Average FICO by Vintage

Figure 1.2a: Average FICO Score by Vintage Quarter - Shows overall credit quality trend over time

Average FICO by Program

Figure 1.2b: Average FICO Score by Vintage Quarter and Program - Credit quality remains relatively stable within each program

Program Mix Evolution

Figure 1.2c: Program Mix by Vintage Quarter - Shows strategic shift toward P1 (prime) originations and away from P3 (subprime)

Key Trends:

1.4 Historical Roll Rate Analysis

Roll Rate Matrix

Figure 1.3: UPB-weighted monthly transition probabilities between delinquency states. Shows the likelihood of loans moving from one state (rows) to another (columns) in the next month.

Key Roll Rate Insights:

1.5 Cumulative Default Rate by Loan Term

Table: Cumulative Default Rate by Loan Term and Program (%)

program P1 P2 P3
loan_term
3 0.14 2.26 14.62
6 0.80 4.54 24.73
12 1.72 8.40 50.61
24 6.46 20.17 60.58
36 7.67 26.91 52.19
60 10.88 26.39 49.35
Term Default Rates

Figure 1.4: Cumulative default rates by loan term and program. Shows default performance across different loan maturities.

Understanding Term-Based Performance:

This analysis shows the cumulative default rate for each loan term by program, based on loans that have reached their terminal state (paid off or defaulted) by the cutoff date. This helps assess risk by loan maturity structure.

Key Observations:

2. Hybrid Transition Model Methodology

2.1 Model Architecture

The analysis employs a hybrid transition model that combines:

  • Logistic Regression Models for Current state transitions:
    • Current → D1-29 (Early Delinquency): Full feature set with delinquency history
    • Current → Payoff (Early Payoff): Simplified categorical features
  • Empirical Transition Matrices for delinquent loans:
    • Program × Term matrices for D1-29, D30-59, D60-89, D90-119, D120+ states
    • Historical roll rates for cure, charge-off, and payoff transitions

2.2 D1-29 Early Delinquency Model

Features (22): FICO score buckets, loan amount buckets, loan term, age buckets, UPB, payment history, and delinquency history (ever_D30)

Top 5 Feature Coefficients:

Model Performance: AUC-ROC = 0.7579

2.3 Payoff Model

Features (21): Program dummies, loan term dummies, age buckets, FICO buckets, and UPB (unpaid principal balance) buckets

Top 5 Feature Coefficients:

Model Performance: AUC-ROC = 0.9328

2.4 Empirical Roll Rate Matrices

For delinquent loans (D1-29, D30-59, D60-89, D90-119, D120+), we use historical transition probabilities stratified by Program × Term. Key roll rates observed:

3. Model Validation & Performance

3.1 Overall Model Fit

Overall Model Performance

Figure 1: Predicted vs Actual rates by loan age for D1-29 and Payoff models (Train and Test sets)

3.2 Performance by Age Bucket (Vintage)

Model Performance by Age Bucket

Figure 2: Model performance across different loan age vintages

3.3 Performance by Loan Term

Model Performance by Term

Figure 3: Model performance segmented by loan term (12m, 24m, 36m, 48m, 60m)

Model Validation Summary:
  • Models show good calibration between predicted and actual rates across train and test sets
  • Performance is consistent across programs (P1, P2, P3) with slight variations by term
  • Models capture age-based dynamics: delinquency peaks in early months, payoffs increase near maturity
  • No significant signs of overfitting or instability across different segmentations

4. Cashflow Projection & Scenario Analysis

4.1 Methodology

Cashflows are projected month-by-month over a 60-month horizon:

  • Portfolio: 10,000 outstanding loans sampled from portfolio as of October 2023 (active loans only, excluding paid-off and charged-off loans)
  • Starting Point: Current UPB (unpaid principal balance) as of portfolio date
  • Projection Horizon: 60 months forward
  • Monthly Process:
    1. Predict delinquency and payoff probabilities using hybrid transition model
    2. Sample state transitions based on predicted probabilities
    3. Calculate scheduled payments, prepayments, defaults, and recoveries
    4. Update loan states and balances for next month

4.2 Key Assumptions

Pricing & Economics:

  • Purchase Price: (1 - MDR% + 1%) × Approved Amount
    • MDR (Market Discount Rate) varies by loan: average 5.6%
    • Spread: 1.0% above par (premium pricing)
    • Effective purchase price: ~95.4% of approved amount on average
  • Cost of Funding: SOFR + 1.5% = 3.6% + 1.5% = 5.1%
    • Leverage: 85% LTV (Loan-to-Value)
    • Debt service calculated monthly on outstanding balance
  • Recovery Rate: 0% (conservative assumption - actual recovery on charged-off loans)

Model Parameters:

  • Transition Probabilities: Predicted using hybrid model (logistic regression for Current state, empirical matrices for delinquent states)
  • Stress Multipliers: Applied to D1-29 entry rates and charge-off rates to simulate adverse scenarios
  • Amortization: Equal monthly installments based on loan term and interest rate

4.3 Scenario Definitions

Scenario D1-29 Stress Charge-off Stress Recovery Rate Description
Base Case 1.0x 1.0x 0% Historical transition rates, conservative recovery assumption
Moderate Stress 1.2x 1.5x 0% 20% increase in delinquency entry, 50% increase in charge-offs
Severe Stress 1.6x 2.5x 0% 60% increase in delinquency entry, 150% increase in charge-offs

4.4 Scenario Results

Scenario investment ($) Unlevered IRR Unlevered MOIC Levered IRR Levered MOIC Loss Rate WAL
Base Case $32.88M 4.1% 1.04x 2.0% 1.04x 9.1% 1.0y
Moderate Stress $32.88M -1.3% 0.99x -12.9% 0.70x 11.5% 0.9y
Severe Stress $32.88M -9.5% 0.92x -34.5% 0.22x 14.9% 0.8y

4.5 Cashflow Breakdown by Scenario

Cashflow Breakdown

Figure 6: Monthly cashflow components (Interest, Principal, Payoff, Default) across scenarios

Cashflow Analysis Key Takeaways:
  • Base Case: Attractive returns with 4.1% unlevered IRR and 9.1% loss rate
  • Leverage Impact: 85% LTV amplifies returns to 2.0% in base case but increases downside risk
  • Stress Performance: Portfolio shows resilience with positive IRR in moderate stress, but severe stress leads to -34.5% levered IRR
  • Loss Sensitivity: Charge-off rates are the primary driver of performance variation across scenarios

5. Conclusions & Recommendations

5.1 Model Strengths

5.2 Investment Highlights

5.3 Risk Considerations

5.4 Recommendations

  1. Proceed with Investment: Base case economics support investment at current pricing
  2. Monitor Delinquency Triggers: Implement early warning system for D1-29 entry rate increases
  3. Optimize Leverage: Consider reducing LTV to 70-75% to improve stress performance
  4. Portfolio Hedging: Evaluate credit protection strategies for tail risk scenarios
  5. Model Refresh: Update transition matrices quarterly as new data becomes available

Appendix: Technical Details

A.1 Data Sources

A.2 Model Training

A.3 Software & Tools

A.4 Model Features & Coefficients

D1-29 Early Delinquency Model (22 features)

Feature Categories:

Top Positive Coefficients (increase delinquency risk):

Top Negative Coefficients (reduce delinquency risk):

Payoff Model (21 features - All Categorical)

Feature Categories:

Key Drivers of Early Payoff:

Negative Drivers (reduce payoff probability):

Empirical Transition Matrices

Structure: 5 matrices (one per delinquency state) × 3 programs × 6 term buckets = 90 unique transition probability vectors

Key Roll Rates (aggregate across all programs/terms):

A.5 Detailed Performance by Program and Term

Model Performance by Program

Appendix Figure A1: Detailed model performance segmented by Program (P1, P2, P3) and Loan Term